This project focuses on understanding how the brain constructs networks of interacting regions (i.e., neural networks) to perform cognitive tasks, especially those associated with audition and language, and how these networks are altered in brain disorders. These issues are addressed by combining computational neuroscience techniques with functional neuroimaging data, obtained using positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) (reviewed in Horwitz, Comptes rendus Biologies, 2005). The network analysis methods allow us to evaluate how brain operations differ between tasks, and between normal and patient populations. This research allows us to ascertain which networks are dysfunctional, and the role neural plasticity plays in enabling compensatory behavior to occur. One study used fMRI to investigate auditory categorization (Husain et al., Human Brain Mapping, in press). We compared the brain responses to a category discrimination task (CAT) with an auditory discrimination task (AUD) using identical sets of sounds. The stimuli differed along a speech-nonspeech dimension and along a fast-slow temporal dynamics dimension. Comparing the activation patterns for CAT relative to AUD, we found that a core group of regions beyond the auditory cortices, including inferior and middle frontal gyri, dorsomedial frontal gyrus, and intraparietal sulcus, were preferentially activated for both familiar (speech) and novel categories. These regions have been shown by others to play a role in working memory. Processing the temporal aspects of the stimuli had a greater impact on the left lateralization of the categorization network than did other factors, particularly in the inferior frontal gyrus, suggesting that there is no inherent left hemisphere advantage in the categorical processing of speech stimuli, or for the categorization task itself. We repeated this study using magnetoencephalography (MEG) (Luo et al., NeuroImage, in press), which permits a finer temporal (but poorer spatial) resolution than does fMRI. Using an induced wavelet transform method, we found in auditory cortex, for both the AUD and CAT conditions, an alpha (8-13Hz) band activation enhancement during the delay period for all stimulus types. A clear difference between the AUD and CAT conditions was observed for the nonspeech stimuli in auditory areas and for both speech and nonspeech stimuli in frontal areas. The results suggest that alpha band activation in auditory areas is related to both working memory and categorization for new nonspeech stimuli. The fact that the dissociation between speech and nonspeech occurred in auditory areas, but not frontal areas, points to different categorization mechanisms and networks for newly learned (nonspeech) and natural (speech) categories. Another major focus of our laboratory seeks to understand the relationship between what is observed in functional neuroimaging studies and the underlying neural dynamics. To do this, we had previously constructed a large-scale computer model of neuronal dynamics that performs a visual object-matching task similar to those designed for PET/fMRI studies (see Horwitz, NeuroInformatics, 2004). Last year, we expanded the model so that it could also simulate auditory processing, thus allowing us to investigate the neural basis of auditory object processing in the cerebral cortex (Husain et al., NeuroImage, 2004). This model relates neuronal dynamics of cortical auditory processing of spectrotemporal patterns to fMRI data. Areas included in the model extend from primary auditory to prefrontal cortex. The electrical activities of the neuronal units of model were constrained to agree with data from the neurophysiological literature. We also conducted an fMRI experiment using stimuli and tasks identical to those used in our simulations. The integrated synaptic activity of the neuronal units of the model was used to determine simulated hemodynamic measures and generally agreed quantitatively with the experimentally observed fMRI data in the brain regions corresponding to the modules of the model. This year, we tested the model by using it to investigate the neural bases of the auditory continuity illusion, a type of perceptual grouping phenomenon (Husain et al., J. Cogn. Neuroscience, 2005). None of the model parameters was changed. Our modeling results agreed with behavioral studies in humans and an electrophysiological study in cats. The results predict that a particular set of bottom-up cortical mechanisms (increasing spectrotemporal receptive fields from primary to secondary to tertiary auditory processing areas that are mediated by anatomical connections) implement the continuity illusion. Environmentally relevant auditory stimuli are often composed of long-duration tonal patterns (e.g., multisyllabic words, short sentences, melodies). Manipulation of those patterns by the brain requires working memory to temporarily store the segments of the pattern and integrate them into a percept. To understand the neural basis of how this is accomplished, we extended the model of auditory recognition of short-duration tonal patterns described above. A memory buffer and a gating module were added. The memory buffer increased the storage capacity; the gating module distributed the segments of the input pattern to separate locations of the memory buffer in an orderly fashion, allowing a subsequent comparison of the stored segments against the segments of a second pattern. Current simulations show that the extended model performs match and mismatch of sequences of long-duration tonal patterns. We conducted an fMRI experiment using the same stimuli as employed in the simulations and found areas in the prefrontal cortex that are likely candidate brain areas for the new modules of the extended model. Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We used our biologically realistic neural models to investigate how neurobiological parameters affect the interregional functional connectivity between fMRI time-series (Horwitz et al., Phil. Trans. Roy. Soc. Lond. B, 2005). Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trial-to- trial interactions with non-specific neurons processing noise stimuli. We found that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during a delayed matching task than during a control task. These results were less clear when the integrated synaptic activity was hemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations.